Example #1
0
def main():
  dataset = pbh.getSupervisedDataSet()
  network = pbh.loadNetwork()
  trainer = BackpropTrainer(network, dataset)

  """
  print("Training network until convergence...")
  trainer.trainUntilConvergence()
  """
  try:
    while(True):
      startTime = time.time()
      error = trainer.train()
      pbh.saveNetwork(network)
      duration = time.time() - startTime
      print("Epoch error: %f after time: %fs" %(error, duration))
  except KeyboardInterrupt:
    print "User stopped training"
Example #2
0
import sys
sys.path.append("./data/")
import time, profile, itertools
import ModifiedTTTBoard as MTT
import minmax
import pybrainHelpers as PBH

network = PBH.loadNetwork()
def neural_network_board_value(board):
    net_input = [unicode(elem, "utf-8") for elem in board.board]
    val = network.activate(net_input)
    return val[0]

def neural_network_minmax(board, depth, alpha=-9999999, beta=9999999):
    neural_network_minmax.nodes_visited += 1

    if board.isLeafNode() or depth == 0:
        return (board.boardScore(), board)

    is_max_board = True if board.getActivePlayer() == 1 else False
    best_child = None
    result = None
    child_ranks = [(child, neural_network_board_value(child)) for child in board.generateChildBoards()]

    if is_max_board == True:
        child_ranks = sorted(child_ranks, key=lambda x: x[1], reverse=True)
        result = alpha
        for child in child_ranks:
            old_result = result
            result = max(result, neural_network_minmax(child[0], depth-1, result, beta)[0])
            if not result == old_result: